Time-integrated Normalized Difference Vegetation Index Research Articles

Spatio-Temporal Description of the NDVI (MODIS) of the Ecuadorian Tussock Grasses and Its Link with the Hydrometeorological Variables and Global Climatic Indices

This study examined the changes in tussock grass greenness over 18 years (2001–2018) using NDVI data from 10 key areas of the Páramo ecosystem in the Ecuadorian Andes. In addition, the study investigated the influence of hydrometeorological variables (precipitation, soil temperature, and water availability) and climatic indices (AAO, MEI, MJO, NAO, PDO, El Niño 1 + 2, 3, 3.4, and 4) on greenness dynamics. The spatial and temporal variations of NDVI were studied, applying several analysis and indicators, such as: the standard deviation, z-score anomalies, Sen slope, Mann–Kendall test, and time integrated-NDVI (TI-NDVI). Linear and multilinear correlations were used to evaluate the influence of hydrometeorological variables and climatic indices on the greenness of tussock. The findings of the study show that Páramo, located in the Inter-Andean valley above 2° S, is the most productive, followed by those located in the Royal Range (eastern cordillera). The anomalies and trends of NDVI on the Royal Range tended to be greening over time. NDVI showed a moderate multilinear correlation with precipitation and soil temperature, and a strong response to water availability. Finally, NDVI was weakly linearly related to the climatic indices, the most representative being the MJO, and slightly related to ENSO events. Understanding the regional and global-scale variables that control tussock grasses’ phenology will help to determine how present and future climate changes will impact this ecosystem.

Sensitivity of forest phenology in China varies with proximity to forest edges

BackgroundShifts in forest phenological events serve as strong indicators of climate change. However, the sensitivity of phenology events to climate change in relation to forest origins has received limited attention. Moreover, it is unknown whether forest phenology changes with the proximity to forest edge. MethodsThis study examined the green-up dates, dormancy dates, time-integrated NDVI (LiNDVI, a measure of vegetation productivity in growing season), and their sensitivities to climatic factors along the gradients of distance (i.e. proximity) to forest edge (0–2 ​km) in China's natural forests (NF) and planted forests (PF). For the analysis, field-surveyed data were integrated with Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI from 2000 to 2022. ResultsOur results reveal that PF had earlier green-up dates, later dormancy dates, and higher LiNDVI than NF. However, green-up sensitivities to temperature were higher at the edges of NF, whereas no such pattern was observed in PF. Conversely, the sensitivity of dormancy dates remains relatively stable from the inner to the edge of both NF and PF, except for a quadratic change in dormancy date sensitivity to precipitation found in NF. Additionally, we found that the green-up sensitivity to temperature increased with decreasing proximity to edge in NF evergreen forests, while it showed the opposite trend in PF evergreen forests. Furthermore, we observed that the precipitation impact on green-up dates shifts from postponing to advancing from the inner to the edge of NF, whereas precipitation dominantly postpones PF's green-up dates regardless of the proximity to edge. The LiNDVI exhibits higher sensitivity to precipitation at the edge areas, a phenomenon observed in NF but not in PF. ConclusionsThese results suggest that the responses of forests to climate change vary with the distance to the edge. With increasing edge forests, which results from fragmentation caused by global changes, we anticipate that desynchronized phenological events along the distance to the edge could alter biogeochemical cycles and reshape ecosystem services such as energy flows, pollination duration, and the tourism industry. Therefore, we advocate for further investigations of edge effects to improve ecosystem modelling, enhance forest stability, and promote sustainable tourism.

Temporal Greenness Trends in Stable Natural Land Cover and Relationships with Climatic Variability across the Conterminous United States

Abstract Assessment of temporal trends in vegetation greenness and related influences aids understanding of recent changes in terrestrial ecosystems and feedbacks from weather, climate, and environment. We analyzed 1-km normalized difference vegetation index (NDVI) time series data (1989–2016) derived from the Advanced Very High Resolution Radiometer (AVHRR) and developed growing-season time-integrated NDVI (GS-TIN) for estimating seasonal vegetation activity across stable natural land cover in the conterminous United States (CONUS). After removing areas from analysis that had experienced land-cover conversion or modification, we conducted a monotonic trend analysis on the GS-TIN time series and found that significant positive temporal trends occurred over 35% of the area, whereas significant negative trends were observed over only 3.5%. Positive trends were prevalent in the forested lands of the eastern one-third of CONUS and far northwest, as well as in grasslands in the north-central plains. We observed negative and nonsignificant trends mainly in the shrublands and grasslands across the northwest, southwest, and west-central plains. To understand the relationship of climate variability with these temporal trends, we conducted partial and multiple correlation analyses on GS-TIN, growing-season temperature, and water-year precipitation time series. The GS-TIN trends in northern forests were positively correlated with temperature. The GS-TIN trends in the central and western shrublands and grasslands were negatively correlated with temperature and positively correlated with precipitation. Our results revealed spatial patterns in vegetation greenness trends for different stable natural vegetation types across CONUS, enhancing understanding gained from prior studies that were based on coarser 8-km AVHRR data. Significance Statement Assessing vegetation trends, cycles, and related influences is important for understanding the responses and feedbacks of terrestrial ecosystems to climatic and environmental changes. We analyzed vegetation greenness trends (1989–2016) for stable natural land cover across the conterminous United States, based on vegetation index time series derived from coarse-resolution optical satellite sensors. We found greening trends in the forests of the east and far northwest and the grasslands of the northern central plains that correlated with increasing temperature in the regions. We observed browning and no trends mainly in the shrublands and grasslands across the northwest, southwest, and western central plains, associated with increasing temperature and decreasing precipitation. Future research should focus on vegetation greenness analysis using finer-resolution satellite data.

Monitoring Climate Impacts on Annual Forage Production across U.S. Semi-Arid Grasslands

The ecosystem performance approach, used in a previously published case study focusing on the Nebraska Sandhills, proved to minimize impacts of non-climatic factors (e.g., overgrazing, fire, pests) on the remotely-sensed signal of seasonal vegetation greenness resulting in a better attribution of its changes to climate variability. The current study validates the applicability of this approach for assessment of seasonal and interannual climate impacts on forage production in the western United States semi-arid grasslands. Using a piecewise regression tree model, we developed the Expected Ecosystem Performance (EEP), a proxy for annual forage production that reflects climatic influences while minimizing impacts of management and disturbances. The EEP model establishes relations between seasonal climate, site-specific growth potential, and long-term growth variability to capture changes in the growing season greenness measured via a time-integrated Normalized Difference Vegetation Index (NDVI) observed using a Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting 19 years of EEP were converted to expected biomass (EB, kg ha−1 year−1) using a newly-developed relation with the Soil Survey Geographic Database range production data (R2 = 0.7). Results were compared to ground-observed biomass datasets collected by the U.S. Department of Agriculture and University of Nebraska-Lincoln (R2 = 0.67). This study illustrated that this approach is transferable to other semi-arid and arid grasslands and can be used for creating timely, post-season forage production assessments. When combined with seasonal climate predictions, it can provide within-season estimates of annual forage production that can serve as a basis for more informed adaptive decision making by livestock producers and land managers.

Climate drivers of Arctic tundra variability and change using an indicators framework

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

Is Alaska’s Yukon–Kuskokwim Delta Greening or Browning? Resolving Mixed Signals of Tundra Vegetation Dynamics and Drivers in the Maritime Arctic

AbstractAlaska’s Yukon–Kuskokwim Delta (YKD) is among the Arctic’s warmest, most biologically productive regions, but regional decline of the normalized difference vegetation index (NDVI) has been a striking feature of spaceborne Advanced High Resolution Radiometer (AVHRR) observations since 1982. This contrast with “greening” prevalent elsewhere in the low Arctic raises questions concerning climatic and biophysical drivers of tundra productivity along maritime–continental gradients. We compared NDVI time series from AVHRR, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat for 2000–19 and identified trend drivers with reference to sea ice and climate datasets, ecosystem and disturbance mapping, field measurements of vegetation, and knowledge exchange with YKD elders. All time series showed increasing maximum NDVI; however, whereas MODIS and Landsat trends were very similar, AVHRR-observed trends were weaker and had dissimilar spatial patterns. The AVHRR and MODIS records for time-integrated NDVI were dramatically different; AVHRR indicated weak declines, whereas MODIS indicated strong increases throughout the YKD. Disagreement largely arose from observations during shoulder seasons, when there is partial snow cover and very high cloud frequency. Nonetheless, both records shared strong correlations with spring sea ice extent and summer warmth. Multiple lines of evidence indicate that, despite frequent disturbances and high interannual variability in spring sea ice and summer warmth, tundra productivity is increasing on the YKD. Although climatic drivers of tundra productivity were similar to more continental parts of the Arctic, our intercomparison highlights sources of uncertainty in maritime areas like the YKD that currently, or soon will, challenge historical concepts of “what is Arctic.”

Soil Salinity Assessment in Irrigated Paddy Fields of the Niger Valley Using a Four-Year Time Series of Sentinel-2 Satellite Images

Salinization is a major soil degradation threat in irrigated systems worldwide. Irrigated systems in the Niger River basin are also affected by salinity, but its spatial distribution and intensity are not currently known. The aim of this study was to develop a method to detect salt-affected soils in irrigated systems. Two complementary approaches were tested: salinity assessment of bare soils using a salinity index (SI) and monitoring of indirect effects of salinity on rice growth using temporal series of a vegetation index (NDVI). The study area was located south of Niamey (Niger) in two irrigated systems of rice paddy fields that cover 6.5 km2. We used remote-sensing and ground-truth data to relate vegetation behavior and reflectance to soil characteristics. We explored all existing Sentinel-2 images from January 2016 to December 2019 and selected cloud-free images on 157 dates that covered eight successive rice-growing seasons. In the dry season of 2019, we also sampled 44 rice fields, collecting 147 biomass samples and 180 topsoil samples from January to June. For each field and growing season, time-integrated NDVI (TI-NDVI) was estimated, and the SI was calculated for dates on which bare soil conditions (NDVI < 0.21) prevailed. Results showed that since there were few periods of bare soil, SI could not differentiate salinity classes. In contrast, the high temporal resolution of Sentinel-2 images enabled us to describe rice-growing conditions over time. In 2019, TI-NDVI and crop yields were strongly correlated (r = 0.77 with total biomass yield and 0.82 with grain yield), while soil electrical conductivity was negatively correlated with both TI-NDVI (r = −0.38) and crop yield (r = −0.23 with total biomass and r = −0.29 with grain yield). Considering the TI-NDVI data from 2016–2019, principal component analysis followed by ascending hierarchical classification identified a typology of five clusters with different patterns of TI-NDVI during the eight growing seasons. When applied to the entire study area, this classification clearly identified the extreme classes (i.e., areas with high or no salinity). Other classes with low TI-NDVI (i.e., during dry seasons) may be related to areas with moderate or seasonal soil salinity. Finally, the high temporal resolution of Sentinel-2 images enabled us to detect stresses on vegetation that occurred repeatedly over the growing seasons, which may be good indicators of soil constraints due to salinity in the context of the irrigated paddy systems of Niger. Further research will validate the ability of the method developed to detect moderate soil salinity constraints over large areas.

Enhancing the positive effects of ecological water conservancy engineering on desert riparian forest growth in an arid basin

In arid areas, ecological water conservancy engineering influences vegetation growth by altering river flooding dynamics in addition to the depths of groundwater. However, few studies have explored how to develop the optimal floodwater and groundwater regulation strategies that take into account both ecological water supply and vegetation growth. Therefore, the aim of the present study was to propose the suitable regulation strategies by revealing that the plant growth responses to hydrological variation (i.e., the flooding and groundwater depth). Based on two growth indicators (i.e., the time-integrated normalized difference vegetation index (TINDVI) for plant canopy growth and the tree-ring chronology for trunk growth) in the middle reaches of the Tarim River, and water consumption, water discharge from ecological sluices for flooding, and groundwater depth data, after the implementation of an scheme in ecological water conservancy engineering, the ecological water transport efficiency, tree-ring chronology in Populus euphratica, and TINDVI improved by 12.4%, 5.6% and 4.2%, respectively. Notably, the tree-ring chronology increased with a decrease in water consumption. Due to a significant correlation between tree-ring chronology and TINDVI, the tree-ring chronology could be used to reconstruct the canopy growth in the case of the unavailability of monitoring data. There were significant correlations between TINDVI and groundwater depth in June–October, between tree-ring chronology and groundwater depth in October, and between water discharge from ecological sluices and vegetation growth in September. In addition, there were high correlation and sensitivity between trunk growth and water discharge (compared to groundwater depth), and between canopy growth and groundwater depth (compared to water discharge). Based on the above results, potentially sustainable groundwater depths (-2.3 to −3.7 m) and discharge water quantities (3.53–5.90 × 108 m3) and regimes were proposed that could further promote the vegetation growth.

Vegetation phenology in Greenland and links to cryospheric change

ABSTRACTRecent greening of vegetation across the Arctic is associated with warming temperatures, hydrologic change and shorter snow-covered periods. Here we investigated trends for a subset of arctic vegetation on the island of Greenland. Vegetation in Greenland is unique due to its close proximity to the Greenland Ice Sheet and its proportionally large connection to the Greenlandic population through the hunting of grazing animals. The aim of this study was to determine whether or not longer snow-free periods (SFPs) were causing Greenlandic vegetation to dry out and become less productive. If vegetation was drying out, a subsequent aim of the study was to determine how widespread the drying was across Greenland. We utilized a 15-year time-series obtained by the MODerate Resolution Imaging Spectroradiometer (MODIS) to analyze the Greenland vegetation by deriving descriptors corresponding with the SFP, the number of cumulative growing degree-days and the time-integrated Normalized Difference Vegetation Index. While the productivity of most vegetated areas increased in response to longer growing periods, there were localized regions that exhibited signs consistent with the drying hypothesis. In these areas, vegetation productivity decreased in response to longer SFPs and more accumulated growing degree-days.

An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI

Information on long-term yield variability is important for tailoring farming practices to the needs of crops. We present a linear mixed-effects model to predict wheat yield at a within-field scale in the northern grain-growing region of Australia. The model predicts yield as a function of Landsat time-integrated Normalized Difference Vegetation Index (iNDVI), and is trained on sparsely sampled yield-monitored data from 17 farms from 2001 to 2016. The model is flexibly parameterized, to use information from specific fields, farms or years, to capture local departures from the global yield-iNDVI relation. We estimated iNDVI numerically by integrating the area under a fitted asymmetric bell-shaped growth function, across the growing season (May to October), using NDVI time-series from April to November. We performed leave-one-out cross-validation, where data from individual farms, fields and years were omitted in a series of tests. Results showed moderate predictive accuracy at a within-field scale, with an average RMSE of 0.79 Mg/ha. The spatial pattern of within-field yield variation was adequately represented. The benefit of the mixed-effects model is that, as well as describing the general relation between wheat yield and iNDVI, it explicitly considers the spatial and temporal differences among farms, fields and years, which are related to variations in soil conditions, farm-management practices and climate. The long-term archive of Landsat imagery – combined with a growing archive of yield maps – provides potential for the model to predict yield variation across large regions and many seasons. Such information could help farmers make decisions on site-specific soil and nutrient management, and simultaneously guide policy-makers towards regional development objectives.

Changing seasonality of panarctic tundra vegetation in relationship to climatic variables

Potential climate drivers of Arctic tundra vegetation productivity are investigated to understand recent greening and browning trends documented by maximum normalized difference vegetation index (NDVI) (MaxNDVI) and time-integrated NDVI (TI-NDVI) for 1982–2015. Over this period, summer sea ice has continued to decline while oceanic heat content has increased. The increases in summer warmth index (SWI) and NDVI have not been uniform over the satellite record. SWI increased from 1982 to the mid-1990s and remained relatively flat from 1998 onwards until a recent upturn. While MaxNDVI displays positive trends from 1982–2015, TI-NDVI increased from 1982 until 2001 and has declined since. The data for the first and second halves of the record were analyzed and compared spatially for changing trends with a focus on the growing season. Negative trends for MaxNDVI and TI-NDVI were more common during 1999–2015 compared to 1982–1998.Trend analysis within the growing season reveals that sea ice decline was larger in spring for the 1982–1998 period compared to 1999–2015, while fall sea ice decline was larger in the later period. Land surface temperature trends for the 1982–1998 growing season are positive and for 1999–2015 are positive in May–June but weakly negative in July–August. Spring biweekly NDVI trends are positive and significant for 1982–1998, consistent with increasing open water and increased available warmth in spring. MaxNDVI trends for 1999–2015 display significant negative trends in May and the first half of June.Numerous possible drivers of early growing season NDVI decline coincident with warming temperatures are discussed, including increased standing water, delayed spring snow-melt, winter thaw events, and early snow melt followed by freezing temperatures. Further research is needed to robustly identify drivers of the spring NDVI decline.

Vegetation phenology gradients along the west and east coasts of Greenland from 2001 to 2015

The objective of this paper is to characterize the spatiotemporal variations of vegetation phenology along latitudinal and altitudinal gradients in Greenland, and to examine local and regional climatic drivers. Time-series from the Moderate Resolution Imaging Spectroradiometer (MODIS) were analyzed to obtain various phenological metrics for the period 2001–2015. MODIS-derived land surface temperatures were corrected for the sampling biases caused by cloud cover. Results indicate significant differences between West and East Greenland, in terms of both observed phenology during the study period, as well as the climatic response. The date of the start of season (SOS) was significantly earlier (24 days), length of season longer (25 days), and time-integrated NDVI higher in West Greenland. The sea ice concentration during May was found to have a significant effect on the date of the SOS only in West Greenland, with the strongest linkage detected in mid-western parts of Greenland.

Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping field into uniformly manageable zones, based on quantitative measurement of yield limiting factors. In Mediterranean environments, the spatial and temporal yield variability of rain-fed cropping system is strongly influenced by the spatial variability of Plant Available Water-holding Capacity (PAWC) and its strong interaction with temporally variable seasonal rainfall. The successful adoption of SSCM depends on the understanding of both spatial and temporal variabilities in cropping fields. Remote sensing phenological metrics provide information about the biophysical growth conditions of crops across fields. In this paper, we examine the potential of phenological metrics to assess the spatial and temporal crop yield variability across a wheat cropping field at Minnipa, South Australia. The Minnipa field was classified into three management zones using prolonged observations including soil assessment and multiple year yield data. The main analytical steps followed in this study were: calculation of the phenological metrics using time series NDVI data from Moderate Resolution Imaging Spectroscope (MODIS) for 15 years (2001-2015); producing spatial trend and temporal variability maps of phenological metrics; and finally, assessment of association between the spatial patterns and temporal variability of the metrics with management zones of the cropping field. The spatial trend of the seasonal peak NDVI metric showed significant association with the management zone pattern. In terms of temporal variability, Time-integrated NDVI (TINDVI) showed higher variability in the “good” zone compared with the “poor” zone. This indicates that the magnitude of the seasonal peak is more sensitive to soil related factors across the field, whereas TINDVI is more sensitive to seasonal variability. The interpretation of the association between phenological metrics and the management zone site conditions was discussed in relation to soil-climate interaction. The results demonstrate the potential of the phenological metrics to assess the spatial and temporal variability across cropping fields and to understand the soil-climate interaction. The approach presented in this paper provides a pathway to utilize phenological metrics for precision agricultural management application.

Assessing ecological vulnerability in western China based on Time-Integrated NDVI data

Arid and semi-arid areas are the most vulnerable regions to climate change. Clear understanding of the effects of climate change on ecosystems in arid and semi-arid regions and the ecosystem vulnerability is important for ecosystem management under the background of climate change. In this study, we conducted a vulnerability assessment on various ecosystems from 1982 to 2013 in western China with large areas of arid and semi-arid lands based on the Time-Integrated Normalized Difference Vegetation Index (TINDVI) data and climate data. The results indicated that grasslands were the most vulnerable ecosystem to climate change in western China, especially for those in Tibetan Plateau. Croplands in oases were not vulnerable to climate change compared to rain-fed croplands in semi-arid regions (e.g. Gansu and Inner Mongolia), which was attributed to the well-developed drip irrigation technology in oases. Desert and Gobi ecosystems were slightly vulnerable to climate change during the past several decades. The assessment results, as revealed in this study, can provide a reference for taking appropriate actions to protect the ecosystems in western China.

Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data

Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available.

Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations

Abstract Accurately estimating aboveground vegetation biomass productivity is essential for local ecosystem assessment and best land management practice. Satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. A 250-m grassland biomass productivity map for the Greater Platte River Basin had been developed based on the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) GSN and Soil Survey Geographic (SSURGO) annual grassland productivity. However, the 250-m MODIS grassland biomass productivity map does not capture detailed ecological features (or patterns) and may result in only generalized estimation of the regional total productivity. Developing a high or moderate spatial resolution (e.g., 30-m) productivity map to better understand the regional detailed vegetation condition and ecosystem services is preferred. The 30-m Landsat data provide spatial detail for characterizing human-scale processes and have been successfully used for land cover and land change studies. The main goal of this study is to develop a 30-m grassland biomass productivity estimation map for central Nebraska, leveraging 250-m MODIS GSN and 30-m Landsat data. A rule-based piecewise regression GSN model based on MODIS and Landsat (r = 0.91) was developed, and a 30-m MODIS equivalent GSN map was generated. Finally, a 30-m grassland biomass productivity estimation map, which provides spatially detailed ecological features and conditions for central Nebraska, was produced. The resulting 30-m grassland productivity map was generally supported by the SSURGO biomass production map and will be useful for regional ecosystem study and local land management practices.

Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration

Abstract. A remote sensing approach is used to examine the direct and indirect effects of snow cover duration and weather conditions on the growth response of mountain grasslands located above the tree line in the French Alps. Time-integrated Normalized Difference Vegetation Index (NDVIint), used as a surrogate for aboveground primary productivity, and snow cover duration were derived from a 13-year long time series of the Moderate-resolution Imaging Spectroradiometer (MODIS). A regional-scale meteorological forcing that accounted for topographical effects was provided by the SAFRAN–CROCUS–MEPRA model chain. A hierarchical path analysis was developed to analyze the multivariate causal relationships between forcing variables and proxies of primary productivity. Inter-annual variations in primary productivity were primarily governed by year-to-year variations in the length of the snow-free period and to a much lesser extent by temperature and precipitation during the growing season. A prolonged snow cover reduces the number and magnitude of frost events during the initial growth period but this has a negligible impact on NDVIint as compared to the strong negative effect of a delayed snow melting. The maximum NDVI slightly responded to increased summer precipitation and temperature but the impact on productivity was weak. The period spanning from peak standing biomass to the first snowfall accounted for two-thirds of NDVIint and this explained the high sensitivity of NDVIint to autumn temperature and autumn rainfall that control the timing of the first snowfall. The ability of mountain plants to maintain green tissues during the whole snow-free period along with the relatively low responsiveness of peak standing biomass to summer meteorological conditions led to the conclusion that the length of the snow-free period is the primary driver of the inter-annual variations in primary productivity of mountain grasslands.

Downscaling 250-m MODIS Growing Season NDVI Based on Multiple-Date Landsat Images and Data Mining Approaches

The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS land surface products robust and reliable. The long-term 30-m Landsat data provide spatial detailed information for characterizing human-scale processes and have been used for land cover and land change studies. The main goal of this study is to combine 250-m MODIS GSN and 30-m Landsat observations to generate a quality-improved high spatial resolution (30-m) GSN database. A rule-based piecewise regression GSN model based on MODIS and Landsat data was developed. Results show a strong correlation between predicted GSN and actual GSN (r = 0.97, average error = 0.026). The most important Landsat variables in the GSN model are Normalized Difference Vegetation Indices (NDVIs) in May and August. The derived MODIS-Landsat-based 30-m GSN map provides biophysical information for moderate-scale ecological features. This multiple sensor study retains the detailed seasonal dynamic information captured by MODIS and leverages the high-resolution information from Landsat, which will be useful for regional ecosystem studies.

Greenlandic sheep farming controlled by vegetation response today and at the end of the 21st Century

The spatial heterogeneity of vegetation greenness and potential aboveground biomass production for sheep farming has been assessed for Southwest Greenland. A Multi-Criteria Evaluation (MCE) model was set up to identify biophysical constraints on the present spatial distribution of farms and fields based on all existing sheep farms in a detailed study area. Time-integrated NDVI (TI-NDVI) from MODIS and observed temperatures (2000–2012) have been combined with a downscaled regional climate model (HIRHAM5) in order to establish a spatio-temporal model for future TI-NDVI, thus forecasting the dry biomass production available for sheep farming in steps of decades for the next 85years. The model has been validated against observed biomass production and the present distribution of fields. Future biomass production is used to discuss the expansion of current farms and to identify new suitable areas for sheep farming. Interestingly, new suitable areas are located where sheep farms were situated during the Norse era more than 1000years ago; areas which have been abandoned for the past 500years. The study highlights the potential of establishing new areas for sheep farming in Arctic Greenland, where current and future climate changes are markedly amplified compared to global trends. However, for the study area the MCE model clearly indicates that the potential of expansion relies on contemporary infrastructural development.

Spatial and temporal variations in ecosystem response to monsoon precipitation variability in southwestern North America

AbstractDue to its marked vegetation phenology and precipitation gradients, the North American Monsoon Region (NAMR) is a useful domain for studying ecosystem responses to climate variability and change. To this end, we analyze long‐term dynamics (1982–2004) in monsoon precipitation (Pr), time‐integrated Normalized Difference Vegetation Index (TINDVI) used as proxy of net primary productivity, and rain‐use efficiency (RUE). The analysis focuses on six ecoregions, spanning from desert environments to tropical dry forests, to investigate (1) how net primary productivity and rain‐use efficiency vary along a precipitation gradient, (2) if interannual variability in net primary productivity is linked to the interannual variability in precipitation, and (3) if there is evidence of a long‐term signal imposed on the interannual variability in rain‐use efficiency. Variations in TINDVI and RUE with Pr along the NAMR precipitation gradient differ among ecoregions exhibiting intensive or extensive water use strategies. We explain the nonlinear behaviors along the precipitation gradient as resulting from different physiological responses to climatological means and the impact of topographic effects. Statistical analysis indicates that the interannual variability in vegetation response is significantly related to the interannual variability in Pr, but their correlation declines with time. A long‐term positive signal in RUE imposed on its interannual variability is identified and results from a constant TINDVI under negative long‐term trends of Pr. This important finding suggests the combined long‐term effects of ecosystem acclimation to reduced water availability and increasing CO2 concentration across the varied ecosystems of the North American Monsoon Region.